Quantitative MR Imaging of Vascular Factors in Parkinsons Disease

摘要 血管健康已被证明是发展的重要因素， 神经退行性疾病，如阿尔茨海默氏症和帕金森氏症。因此，有能力 可以可靠和定量地测量老化大脑中的早期血液动力学变化， 诊断、研究和开发治疗方法的强大工具。 动脉自旋标记（ASL）磁共振成像可以产生定量灌注图像 而不使用造影剂。我们建议结合新的ASL技术，如 速度选择性反转（VSI）标记脉冲和磁共振指纹 (MRF)使用深度学习回归方法， 血流动力学参数超出灌注，从而提供了一个更微妙的图片， 血管系统的状态。我们还预计，新技术将提供戏剧性的 提高信噪比，特异性和灵敏度的ASL，并提出的技术将 在研究和临床上有许多其他应用。 我们建议使用这些技术，以填补知识差距的关系， 血管变化和帕金森病及其症状，特别是疲劳， 发病机制还不清楚。如果我们在这个应用程序中取得成功，未来的工作将使用 将感兴趣的血液动力学参数作为评估神经变性风险的生物标志物， 确定治疗目标，并指导新疗法的开发。

Abstract Vascular health has been shown to be an important factor in the development of neurodegenerative diseases like Alzheimer’s and Parkinson’s diseases. Hence, the ability to measure reliably and quantitatively early hemodynamic changes in the aging brain can be a powerful tool for diagnosing, studying, and developing treatments. Arterial Spin Labeling (ASL) magnetic resonance imaging can yield quantitative perfusion images without the use of contrast agents. We propose that combining new ASL techniques, such as Velocity Selective Inversion (VSI) labeling pulses and magnetic resonance fingerprinting (MRF) with deep learning regression methods will allow quantification of multiple hemodynamic parameters beyond perfusion, thus providing a much more nuanced picture of the state of the vasculature. We also expect that the new technique will offer dramatic improvements in SNR, specificity and sensitivity of ASL, and that the proposed techniques will have many other applications in research and in the clinic. We propose to use these techniques to fill the knowledge gap regarding the relationship between vascular changes and Parkinson’s disease and its symptoms, particularly fatigue, whose pathogenesis is not well understood. If we are successful in this application, future work will use the hemodynamic parameters of interest as biomarkers to assess risk of neurodegeneration, determine therapeutic targets, and guide in the development of new therapies.